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formulate_recipe

Create a multi-ingredient recipe to calculate aggregate and per-serving macros, calorie split, amino acid completeness, micronutrient ratios, glycemic impact, and NOVA cleanliness score.

Instructions

Formulates a multi-ingredient recipe and returns aggregate and per-serving macros,

caloric distribution, amino acid completeness (WHO/FAO 2007 pattern + 2.5g Leucine MPS threshold), micronutrient synergies (Na:K ratio, Ca:Mg ratio, Iron bioavailability), glycemic impact (GI and Glycemic Load), and cleanliness score (weighted NOVA).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
titleNo
servingsNo
ingredientsYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

C2.4/5.0
Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description must carry the full behavioral burden. It discloses the computed outputs but says nothing about permissions, side effects, whether it is read-only or computational, or any execution constraints. This leaves significant behavioral gaps.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness3/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single front-loaded sentence that begins with the tool's core purpose. However, it then enumerates a long list of return values that are likely already defined by the output schema, so not every phrase earns its place.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The tool is complex and has no annotations, yet the description focuses almost entirely on return values, which the output schema already covers. It omits usage context and parameter semantics, making it insufficient for an agent to invoke the tool correctly without opening the schema.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters1/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, and the description adds no information about the three input parameters. It does not explain the expected structure of the ingredients array or the role of servings and title, leaving the schema's undocumented parameters entirely unclarified.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description states a specific verb and resource: it formulates a multi-ingredient recipe. It also clearly enumerates the types of nutritional analysis returned. However, it does not distinguish itself from the sibling optimize_recipe_macros or other related tools, so a 4 rather than 5.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides no guidance on when to use this tool versus alternatives such as optimize_recipe_macros or lookup_food. It describes what the tool returns but not the context or conditions for selecting it.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.